Photon counting detectors (PCD) can provide spectral information to enable iodine quantification through multi-energy imaging but performance is limited by current PCD technology. The purpose of this work is to evaluate iodine quantification in a phantom study using dual-source PCD-CT (DS-PCD-CT), and compare to single-source (SS)-PCD-CT and traditional DS energy integrating detector (EID)-based dual-energy CT. A multi-energy CT phantom with iodine inserts (0 to 15 mg ml−1 concentration) was imaged ona research SS-PCD-CT scanner (CTDIvol = 18 mGy). A DS-PCD-CT was emulated by acquiring two sequential scans (CTDIvol = 9 mGy each) using tube potentials: 140 kVp/80 kVp, 140 kVp/100 kVp and 140 kVp/120 kVp. For each kVp, 1 or 2 energy bins were reconstructed to achieve either dual-energy or quadruple energy CT. In addition to these energy combinations, a Sn filter was used for the high tube potential (140 kVp) of each kVp pair. For comparison, the same phantom was also scanned on a commercially available DS-EID-CT with matched radiation dose (CTDIvol = 18 mGy). Material decomposition was performed in image space using a standard least-squares based approach to generate iodine and water-specific images. The root-mean-square-error (RMSE) measured over each insert from the iodine image was used to determine iodine accuracy. The iodine RMSE from SS-PCD (140 kVp with 2 energy bins) was 2.72 mg ml−1. The use of a DS configuration with 1 energy bin per kVp (140 kVp/80 kVp) resulted in a RMSE of 2.29 mg ml−1. Two energy bins per kVp further reduced iodine RMSE to 1.83 mg ml−1. The addition of a Sn filter to the latter quadruple energy mode reduced RMSE to 1.48 mg ml−1. RMSE for DS-PCD-CT (2 energy bins per kVp) decreased by 1.3% (Sn140 kVp/80 kVp) and 15% (Sn140 kVp/100 kVp) as compared to DS-EID-CT. DS-PCD-CT with a Sn filter improved iodine quantification as compared to both SS-PCD-CT and DS-EID-CT.
Assessment of x-ray angiography system performance is typically performed using stationary test objects with simple geometries such as a disk on a uniform background. However, these methods do not represent realistic imaging conditions in interventional cardiology as anatomy and devices are inherently non-stationary due to cardiac motion. In this work, a novel implementation of the channelized Hotelling observer (CHO) was used to assess the influence of motion blur on object detectability. A standard CHO model assumes imaging system stationarity whereby the detectability index of a test object is independent of location. However, real angiography systems are inherently non-stationary. While vendor correction gain factors and offset maps are used to compensate for visual non-uniformities, these corrections do not restore stationarity to the images. Methods to accommodate non-stationarity and allow assessment of the influence of motion blur on test object detectability will be presented. The effect of motion blur was quantified with the relative detectability index (), where the for an object when moving with constant linear velocity was compared to a low velocity ‘pseudo-stationary’ condition to account for system non-stationarity. The pseudo-stationary condition was used to isolate the influences of spatial non-stationarity and motion blur. Three different test object shapes (disks, spheres and capsules) with linear velocity in the range 0–30 cm · s−1 were tested. For 1 mm diameter objects and linear velocity 30 cm · s−1, was degraded by 37%, 33% and 42% for the disk, sphere and capsule respectively, relative to the pseudo-stationary condition. Considering all test objects with diameter greater than 2 mm and linear velocity 30 cm · s−1, was degraded by less than 10% due to motion. In summary, this work describes a new approach to assess performance of x-ray angiography systems using the CHO model and moving test objects.
BACKGROUND Gradient non-linearity (GNL) leads to biased apparent diffusion coefficients (ADCs) in diffusion weighted imaging. A gradient non-linearity correction (GNLC) method has been developed for whole body systems, but yet to be tested for the new compact 3T (C3T) scanner which exhibits more complex GNL due to its asymmetrical design. PURPOSE To assess the improvement of ADC quantification with GNLC for the C3T scanner. STUDY TYPE Phantom measurements and retrospective analysis of patient data. PHANTOM/SUBJECTS A diffusion quality control phantom with vials containing 0-30% polyvinylpyrrolidone in water was used. For in-vivo data, 12 patient exams were analyzed (median age =33). FIELD STRENGTH/SEQUENCE Imaging was performed on the C3T and two commercial 3T scanners. A clinical DWI (TR = 10000 ms, TE = min, b = 1000 s/mm2) sequence was used for phantom imaging and 10 patient cases and a clinical DTI (TR = 6000-10000 ms, TE = min, b = 1000s/mm2) sequence was used for two patient cases. ASSESSMENT The 0% vial was measured along three orthogonal axes, and at two different temperatures. The ADC for each concentration was compared between the C3T and two whole body scanners. Cerebrospinal fluid and white matter ADCs were quantified for patient and compared to values in literature. STATISTICAL TEST Paired t-test and two-way ANOVA RESULTS For all PVP concentrations, the corrected ADC was within 2.5% of the reference ADC. On average, the ADC of cerebrospinal fluid and white matter post-GNLC were within 1% and 6% of values reported in literature respectively and were significantly different from the uncorrected data (p<0.05). DATA CONCLUSION This study demonstrated that GNL effects were more severe for the C3T due to the asymmetric gradient design, but our implementation of a GNLC compensated for these effects, resulting in ADC values that are in good agreement with values from literature.
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